Showing papers by "Northeastern University (China) published in 2019"

Global diversity and biogeography of bacterial communities in wastewater treatment plants

Abstract: Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, the diversity of microorganisms and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analysed the 16S ribosomal RNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small, global core bacterial community (n = 28 operational taxonomic units) that is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal and drift), although deterministic factors (temperature and organic input) are also important. Our findings enhance our mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes.

Traditional Chinese medicine as a cancer treatment: Modern perspectives of ancient but advanced science.

Abstract: Traditional Chinese medicine (TCM) has been practiced for thousands of years and at the present time is widely accepted as an alternative treatment for cancer In this review, we sought to summarize the molecular and cellular mechanisms underlying the chemopreventive and therapeutic activity of TCM, especially that of the Chinese herbal medicine-derived phytochemicals curcumin, resveratrol, and berberine Numerous genes have been reported to be involved when using TCM treatments and so we have selectively highlighted the role of a number of oncogene and tumor suppressor genes in TCM therapy In addition, the impact of TCM treatment on DNA methylation, histone modification, and the regulation of noncoding RNAs is discussed Furthermore, we have highlighted studies of TCM therapy that modulate the tumor microenvironment and eliminate cancer stem cells The information compiled in this review will serve as a solid foundation to formulate hypotheses for future studies on TCM-based cancer therapy

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Abstract: Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

Distributed Adaptive Event-Triggered Fault-Tolerant Consensus of Multiagent Systems With General Linear Dynamics

Abstract: In this paper, the distributed adaptive event-triggered fault-tolerant consensus of general linear multiagent systems (MASs) is considered. First, in order to deal with multiplicative fault, a distributed event-triggered consensus protocol is designed. Using distributed adaptive online updating strategies, the computation of the minimum eigenvalue of Laplacian matrix is avoided. Second, some adaptive parameters are introduced in trigger function to improve the self-regulation ability of event-triggered mechanism. The new trigger threshold is both state-dependent and time-dependent, which is independent of the number of agents. Then sufficient conditions are derived to guarantee the leaderless and leader-following consensus. On the basis of this, the results are extended to the case of actuator saturation. It is proved the Zeno-behavior of considered event-triggered mechanism is avoided. At last, the effectiveness of the proposed methods are demonstrated by three simulation examples.

Deep residual learning-based fault diagnosis method for rotating machinery

Abstract: Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.

Is Cobalt Needed in Ni-Rich Positive Electrode Materials for Lithium Ion Batteries?

Abstract: As a derivative of LiNiO2, NCA (LiNi1-x-yCoxAlyO2) is widely used in the electric vehicle industry because of its high energy density. It is thought that Co and Al both play important roles in enhancing NCA material properties. However, there is no solid evidence in the literature that clearly shows that Co is required in NCA with high nickel (e.g. when 1-x-y > 0.9) content. Therefore, a systematic study on the roles of different cation substituents in a series of LiNi1-nMnO2 (M = Al, Mn, Mg, or Co) materials was made. In-situ X-ray diffraction (XRD) and differential capacity versus voltage (dQ/dV vs. V) studies showed that the multiple phase transitions in LixNiO2 during charge and discharge, thought to cause poor charge-discharge capacity retention, were suppressed in LixNi0.95M0.05O2 (M = Al, Mn, or Mg), while 5% Co failed to suppress the phase transitions. First principles calculations were made to understand the function of each substituent. Accelerating rate calorimetry shows that unlike Al, Mn, or Mg, Co has no contribution to safety improvement. Therefore, we believe that Co brings little or no value at all to NCA-type materials with high Ni content (> 90% Ni in the transition metal layer) and we hope this paper will spur more interest in Co-free materials.

Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow

Abstract: Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.

Decentralized Adaptive Fuzzy Secure Control for Nonlinear Uncertain Interconnected Systems Against Intermittent DoS Attacks

Abstract: Cyber-physical systems (CPSs) are naturally highly interconnected and complexly nonlinear. This paper investigates the problem of decentralized adaptive output feedback control for CPSs subject to intermittent denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear uncertain strict-feedback interconnected systems. When a DoS attack is active, all the state variables become unavailable and standard backstepping cannot be applied. To overcome this difficulty, a switching-type adaptive state estimator is constructed. Based on an improved average dwell time method incorporated by frequency and duration properties of DoS attacks, convex design conditions of controller parameters are derived in term of solving a set of linear matrix inequalities. The proposed controller guarantees that all closed-loop signals remain bounded, while the error signals converge to a small neighborhood of the origin. As an illustrative example, the proposed control scheme is applied to a power network system.

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

Abstract: A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.

Shaping triple-conducting semiconductor BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ into an electrolyte for low-temperature solid oxide fuel cells

Abstract: Interest in low-temperature operation of solid oxide fuel cells is growing. Recent advances in perovskite phases have resulted in an efficient H+/O2-/e- triple-conducting electrode BaCo0.4Fe0.4Zr0.1Y0.1O3-δ for low-temperature fuel cells. Here, we further develop BaCo0.4Fe0.4Zr0.1Y0.1O3-δ for electrolyte applications by taking advantage of its high ionic conduction while suppressing its electronic conduction through constructing a BaCo0.4Fe0.4Zr0.1Y0.1O3-δ-ZnO p-n heterostructure. With this approach, it has been demonstrated that BaCo0.4Fe0.4Zr0.1Y0.1O3-δ can be applied in a fuel cell with good electrolyte functionality, achieving attractive ionic conductivity and cell performance. Further investigation confirms the hybrid H+/O2- conducting capability of BaCo0.4Fe0.4Zr0.1Y0.1O3-δ-ZnO. An energy band alignment mechanism based on a p-n heterojunction is proposed to explain the suppression of electronic conductivity and promotion of ionic conductivity in the heterostructure. Our findings demonstrate that BaCo0.4Fe0.4Zr0.1Y0.1O3-δ is not only a good electrode but also a highly promising electrolyte. The approach reveals insight for developing advanced low-temperature solid oxide fuel cell electrolytes.

Generalized Multitasking for Evolutionary Optimization of Expensive Problems

Abstract: Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing MFEA is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization.

An Unavoidable Challenge for Ni-Rich Positive ElectrodeMaterials for Lithium-Ion Batteries

Abstract: LiNi1–x–yCoxAlyO2 (NCA) and LiNi1–x–yMnxCoyO2 (NMC) materials are widely used in electric vehicle and energy storage applications. Derived from LiNiO2, NCA and NMC materials with various chemistrie...

Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting

Abstract: Electric load forecasting has always been a key component of power grids. Many countries have opened up electricity markets and facilitated the participation of multiple agents, which create a competitive environment and reduce costs to consumers. In the electricity market, multi-step short-term load forecasting becomes increasingly significant for electricity market bidding and spot price calculation, but the performances of traditional algorithms are not robust and unacceptable enough. In recent years, the rise of deep learning gives us the opportunity to improve the accuracy of multi-step forecasting further. In this paper, we propose a novel model multi-scale convolutional neural network with time-cognition (TCMS-CNN). At first, a deep convolutional neural network model based on multi-scale convolutions (MS-CNN) extracts different level features that are fused into our network. In addition, we design an innovative time coding strategy called the periodic coding strengthening the ability of the sequential model for time cognition effectively. At last, we integrate MS-CNN and periodic coding into the proposed TCMS-CNN model with an end-to-end training and inference process. With ablation experiments, the MS-CNN and periodic coding methods had better performances obviously than the most popular methods at present. Specifically, for 48-step point load forecasting, the TCMS-CNN had been improved by 34.73%, 14.22%, and 19.05% on MAPE than the state-of-the-art methods recursive multi-step LSTM (RM-LSTM), direct multi-step MS-CNN (DM-MS-CNN), and the direct multi-step GCNN (DM-GCNN), respectively. For 48-step probabilistic load forecasting, the TCMS-CNN had been improved by 3.54% and 6.77% on average pinball score than the DM-MS-CNN and the DM-GCNN. These results show a great promising potential applied in practice.